Open Access
Issue
MATEC Web Conf.
Volume 280, 2019
The 5th International Conference on Sustainable Built Environment (ICSBE 2018)
Article Number 05023
Number of page(s) 10
Section Sustainable Resource Management
DOI https://doi.org/10.1051/matecconf/201928005023
Published online 08 May 2019
  1. UNESCO, “Decision of the Intergovernmental Committee: 4.COM 13.28 - intangible heritage - Culture Sector - UNESCO,” Fourth Session of the Intergovernmental Committee - Abu Dhabi, United Arab Emirates, 2009. [Online]. Available: http://www.unesco.org/culture/ich/en/decisions/4.COM/13.28. [Accessed: 18-Jan- 2018]. [Google Scholar]
  2. PPID Banjarmasin, “Kain Sasirangan Banjarmasin | PPID Kota Banjarmasin.” [Online]. Available: http://ppid.banjarmasinkota.go.id/2017/01/kain-sasirangan- banjarmasin.html. [Accessed: 20-Jan-2018]. [Google Scholar]
  3. A. A. Kasim and A. Harjoko, “Klasifikasi Citra Batik Menggunakan Jaringan Syaraf Tiruan Berdasarkan Gray Level Co- Occurrence Matrices (GLCM),” Semin. Nas. Apl. Teknol. Inf. Yogyakarta, 21 Juni 2014, pp. 7–13, 2014. [Google Scholar]
  4. A. E. Minarno and N. Suciati, “Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurrence Matrix,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 12, no. 3, p. 597, Sep. 2014. [CrossRef] [Google Scholar]
  5. A. E. Minarno, Y. Munarko, A. Kurniawardhani, F. Bimantoro, and N. Suciati, “Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification,” in 2014 2nd International Conference on Information and Communication Technology, ICoICT 2014, 2014, pp. 249–254. [Google Scholar]
  6. C. S. K. Aditya, M. Hani’Ah, R. R. Bintana, and N. Suciati, “Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction,” in Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015, 2016, pp. 163–167. [Google Scholar]
  7. Q. Chen and E. Agu, “Exploring Statistical GLCM Texture Features for Classifying Food Images,” in 2015 International Conference on Healthcare Informatics, 2015, pp. 453–453. [CrossRef] [Google Scholar]
  8. M. D. Rosyadi, “Pengenalan Motif Dasar Pada Kain Sasirangan Menggunakan Metode Template Matching,” Technol. J. Ilm., vol. 8, no. 2, pp. 53–61, 2017. [Google Scholar]
  9. I. Setyawan, I. K. Timotius, and M. Kalvin, “Automatic batik motifs classification using various combinations of SIFT features moments and k-Nearest Neighbor,” in Proceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering, ICITEE 2015, 2015, pp. 269–274. [Google Scholar]
  10. N. Suciati, W. A. Pratomo, and D. Purwitasari, “Batik motif classification using color- texture-based feature extraction and backpropagation neural network,” in Proceedings - 2014 IIAI 3 rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014, 2014, pp. 517–521. [Google Scholar]
  11. C. S. K. Aditya, M. Hani’Ah, R. R. Bintana, and N. Suciati, “Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction,” in Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015, 2016, pp. 163–167. [Google Scholar]
  12. R. Azhar, D. Tuwohingide, D. Kamudi, Sarimuddin, and N. Suciati, “Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine,” Procedia Comput. Sci., vol. 72, pp. 24–30, Jan. 2015. [CrossRef] [Google Scholar]
  13. S. Seman, Sasirangan: kain khas Banjar. Lembaga Pengkajian dan Pelestarian, Budaya Banjar, Kalimantan Selatan, 2008. [Google Scholar]
  14. A. Vedaldi and B. Fulkerson, “VLFeat: An open and portable library of computer vision algorithms (2008).” 2012. [Google Scholar]

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